Evolution of language: Lessons from the genome

The post-genomic era is an exciting time for researchers interested in the biology of speech and language. Substantive advances in molecular methodologies have opened up entire vistas of investigation that were not previously possible, or in some cases even imagined. Speculations concerning the origins of human cognitive traits are being transformed into empirically addressable questions, generating specific hypotheses that can be explicitly tested using data collected from both the natural world and experimental settings. In this article, I discuss a number of promising lines of research in this area. For example, the field has begun to identify genes implicated in speech and language skills, including not just disorders but also the normal range of abilities. Such genes provide powerful entry points for gaining insights into neural bases and evolutionary origins, using sophisticated experimental tools from molecular neuroscience and developmental neurobiology. At the same time, sequencing of ancient hominin genomes is giving us an unprecedented view of the molecular genetic changes that have occurred during the evolution of our species. Synthesis of data from these complementary sources offers an opportunity to robustly evaluate alternative accounts of language evolution. Of course, this endeavour remains challenging on many fronts, as I also highlight in the article. Nonetheless, such an integrated approach holds great potential for untangling the complexities of the capacities that make us human.     

On the representation of multiple languages in the brain: old problems and new directions

This paper has attempted to show that when the relevant literature is critically scrutinized there is very little evidence for the idea that the right hemisphere participates in multiple languages in any way significantly different from the case of a single language. We have seen that most of the clinical data support the simple notion that the left hemisphere controls the first and subsequent languages to the same degree. The experimental data are full of contradictions, but many of the claims are invalid due to various methodological and analytical problems explained previously. It is suggested that future research concentrate on finer-grained questions than simply whether a language is left lateralized or not. These questions should make connections with other known neurological and neuropsychological facts. Of primary interest is obtaining converging evidence on the hypothesis of differential cerebral territories within the left hemisphere of bilinguals for different languages. In conclusion, we would be wise to consider the bilingual as a very rich potential source for testing various hypotheses about linguistic and neurolinguistic organization. But we should not lose the opportunity to test both more general and more specific hypotheses to which the multilingual gives us access. If treated with care the multilingual will tell us much, even though he may speak in many tongues.     

Bayes factors for the linear ballistic accumulator model of decision-making

Evidence accumulation models of decision-making have led to advances in several different areas of psychology. These models provide a way to integrate response time and accuracy data, and to describe performance in terms of latent cognitive processes. Testing important psychological hypotheses using cognitive models requires a method to make inferences about different versions of the models which assume different parameters to cause observed effects. The task of model-based inference using noisy data is difficult, and has proven especially problematic with current model selection methods based on parameter estimation. We provide a method for computing Bayes factors through Monte-Carlo integration for the linear ballistic accumulator (LBA; Brown and Heathcote, 2008), a widely used evidence accumulation model. Bayes factors are used frequently for inference with simpler statistical models, and they do not require parameter estimation. In order to overcome the computational burden of estimating Bayes factors via brute force integration, we exploit general purpose graphical processing units; we provide free code for this. This approach allows estimation of Bayes factors via Monte-Carlo integration within a practical time frame. We demonstrate the method using both simulated and real data. We investigate the stability of the Monte-Carlo approximation, and the LBA’s inferential properties, in simulation studies.     

“动手不动口”：手部动作与语言进化的关系

语言进化是进化心理学研究领域的重要问题。镜像系统假说、工具制造假说与传授假说从不同角度对手部动作与语言进化间的关系进行了解释, 三种假说都认为人类语言起源于手部动作经验。相关实证研究发现：手语与口语具有一致性特征、语言与手部动作具有共同的神经基础、手势发展可以预测语言发展水平以及手势可以提高工具制造知识的传播效率, 这些研究为三种假说的具体观点提供了实证支持。未来该领域的研究需要关注手势语与口语在进化中的发展关系, 以及人类语言进化与其他认知特征的进化关系。     

Medical diagnostic reasoning: Epistemological modeling as a strategy for design of computer-based consultation programs

The complexity of cognitive emulation of human diagnostic reasoning is the major challenge in the implementation of computer-based programs for diagnostic advice in medicine. We here present an epistemological model of diagnosis with the ultimate goal of defining a high-level language for cognitive and computational primitives. The diagnostic task proceeds through three different phases: hypotheses generation, hypotheses testing and hypotheses closure. Hypotheses generation has the inferential form of abduction (from findings to hypotheses) constrained under the criterion of plausibility. Hypotheses testing is achieved by a deductive inference (from generated hypotheses to expected findings), followed by an eliminative induction, constrained under the criterion of covering, which matches expected findings against patient's findings to select the best explanation. Hypotheses closure is a deductive-inductive type of inference very similar to the inferences operating in hypotheses testing. In this case induction matches the consequences of the generated hypotheses against the patient's characteristics or preferences under the criterion of utility. By using the language exploited in this epistemological model, it is possible to describe the cognitive tasks underlying the most influential knowledge-based diagnostic systems.     

An overview of Bayesian reasoning in the analysis of delay‐discounting data

Statistical inference (including interval estimation and model selection) is increasingly used in the analysis of behavioral data. As with many other fields, statistical approaches for these analyses traditionally use classical (i.e., frequentist) methods. Interpreting classical intervals and p‐values correctly can be burdensome and counterintuitive. By contrast, Bayesian methods treat data, parameters, and hypotheses as random quantities and use rules of conditional probability to produce direct probabilistic statements about models and parameters given observed study data. In this work, we reanalyze two data sets using Bayesian procedures. We precede the analyses with an overview of the Bayesian paradigm. The first study reanalyzes data from a recent study of controls, heavy smokers, and individuals with alcohol and/or cocaine substance use disorder, and focuses on Bayesian hypothesis testing for covariates and interval estimation for discounting rates among various substance use disorder profiles. The second example analyzes hypothetical environmental delay‐discounting data. This example focuses on using historical data to establish prior distributions for parameters while allowing subjective expert opinion to govern the prior distribution on model preference. We review the subjective nature of specifying Bayesian prior distributions but also review established methods to standardize the generation of priors and remove subjective influence while still taking advantage of the interpretive advantages of Bayesian analyses. We present the Bayesian approach as an alternative paradigm for statistical inference and discuss its strengths and weaknesses.     

The evolution of speech: a comparative review

The evolution of speech can be studied independently of the evolution of language, with the advantage that most aspects of speech acoustics, physiology and neural control are shared with animals, and thus open to empirical investigation. At least two changes were necessary prerequisites for modern human speech abilities: (1) modification of vocal tract morphology, and (2) development of vocal imitative ability. Despite an extensive literature, attempts to pinpoint the timing of these changes using fossil data have proven inconclusive. However, recent comparative data from nonhuman primates have shed light on the ancestral use of formants (a crucial cue in human speech) to identify individuals and gauge body size. Second, comparative analysis of the diverse vertebrates that have evolved vocal imitation (humans, cetaceans, seals and birds) provides several distinct, testable hypotheses about the adaptive function of vocal mimicry. These developments suggest that, for understanding the evolution of speech, comparative analysis of living species provides a viable alternative to fossil data. However, the neural basis for vocal mimicry and for mimesis in general remains unknown.     

有性Penna模型:进化心理学的新工具

进化心理学家以进化论为基础,使用了许多不同的方法来研究人类的心灵。这些方法都有共同的缺陷,即无法直接对提出的假设进行验证,对复杂的系统进行研究难度很大,计算机模拟的方法可在一定程度上修补该问题。有性Penna模型能反映有性生殖种群的进化特点,能反映环境对生物进化的影响,通过该模型来模拟人类进化历程,则可以检验某一心理机制是否是适应的,它是研究生物进化的有力工具。本文在综述已有研究成果的基础上提出用有性Penna模型对进化心理学关于进化轨迹的假设进行验证的构想,并在此基础之上提出了用该模型进行研究的基本框架。     

Children's category‐based inferences affect classification

Children learn many new categories and make inferences about these categories. Much work has examined how children make inferences on the basis of category knowledge. However, inferences may also affect what is learned about a category. Four experiments examine whether category‐based inferences during category learning influence category knowledge and thereby affect later classifications for 5‐ to 7‐year‐olds. The children learned to classify pictures of new types of creatures on the basis of a salient feature (colour) and then answered a question that required them to make an inference on the basis of other features. At test, children classified pictures that included only some features (without colour). Experiment 1 showed that the features relevant to the inference during learning led to better classification than did features irrelevant to the inference. Experiment 2 replicated this finding even when the relevant features were physically close to the irrelevant features. Experiments 3 and 4 found this effect even when the classification was learned prior to the inference task and even when no mention was made of the categories during inference learning. Taken together, these results show that making inferences during category learning can influence category knowledge and suggest a need to integrate the work on category learning and category‐based inferences.     

变量相对重要性评估的方法选择及应用

高维数据爆发的背景下,心理学研究目前急需变量相对重要性评估的有效方法。相对重要性评估的关键是选择合适的评估指标和统计推断方法。相对重要性的评估指标种类繁多,优势分析和相对权重是重点推荐的相对重要性评估指标。相对重要性的统计推断方法适用情境不同,Bootstrap抽样是推断单变量重要性和两变量重要性差异的常用方法,而贝叶斯检验是评估多变量重要性次序的新方法。线性回归模型之外,相对重要性研究已拓展到Logistic回归模型、结构方程模型、多水平模型等,但适用数据类型仍较为有限。相对重要性评估已广泛应用于心理学实证研究,但存在不恰当的指标解释和方法选择问题。为此,结合具体例子说明变量相对重要性的评估过程。     

Comments on R.M. Grieger's “contextual model of the ABCs of RET”

Grieger's (1985) contextual model of the ABCs of RET is examined and found to have many interesting and thought-provoking aspects. Its hypotheses in regard to emphasizing various therapeutic aspects of RET are evaluated and found to make good sense but sometimes to be stated in an extreme manner that may lead to questionable results.     

The Value of Symbolic Computation

Standard generative linguistic theory, which uses discrete symbolic models of cognition, has some strengths and weaknesses. It is strong on providing a network of outposts that make scientific travel in the jungles of natural language feasible. It is weak in that it currently depends on the elaborate and unformalized use of intuition to develop critical supporting assumptions about each data point. In this regard, it is not in a position to characterize natural language systems in the lawful terms that ecological psychologists strive for. Connectionist learning models offer some help: They define lawful relations between linguistic environments and language systems. But our understanding of them is currently weak, especially when it comes to natural language syntax. Fortunately, symbolic linguistic analysis can help connectionism if the two meet via dynamical systems theory. I discuss a case in point: Insights from linguistic explorations of natural language syntax appear to have identified information structures that are particularly relevant to understanding ecologically appealing but analytically mysterious connectionist learning models.     

A general Bayesian multilevel multidimensional IRT model for locally dependent data

Many item response theory (IRT) models take a multidimensional perspective to deal with sources that induce local item dependence (LID), with these models often making an orthogonal assumption about the dimensional structure of the data. One reason for this assumption is because of the indeterminacy issue in estimating the correlations among the dimensions in structures often specified to deal with sources of LID (e.g., bifactor and two-tier structures), and the assumption usually goes untested. Unfortunately, the mere fact that assessing these correlations is a challenge for some estimation methods does not mean that data seen in practice support such orthogonal structure. In this paper, a Bayesian multilevel multidimensional IRT model for locally dependent data is presented. This model can test whether item response data violate the orthogonal assumption that many IRT models make about the dimensional structure of the data when addressing sources of LID, and this test is carried out at the dimensional level while accounting for sampling clusters. Simulations show that the model presented is effective at carrying out this task. The utility of the model is also illustrated on an empirical data set.     

Information relevance, working memory, and the consideration of alternatives

People routinely focus on one hypothesis and avoid consideration of alternative hypotheses on problems requiring decisions between possible states of the world--for example, on the “pseudodiagnosticity” task (Doherty, Mynatt, Tweney, & Schiavo, 1979). In order to account for behaviour on such “inference” problems, it is proposed that people can hold in working memory, and operate upon, but one alternative at a time, and that they have a bias to test the hypothesis they think true. In addition to being an ex post facto explanation of data selection in inference tasks, this conceptualization predicts that there are situations in which people will consider alternatives. These are:

1. “action” problems, where the alternatives are possible courses of action;

2. “inference” problems, in which evidence favours an alternative hypothesis.

Experiment 1 tested the first prediction. Subjects were given action or inference problems, each with two alternatives and two items of data relevant to each alternative. They received probabilistic information about the relation between one datum and one alternative and picked one value from among the other three possible pairs of such relations. Two findings emerged; (1) a strong tendency to select information about only one alternative with inferences; and (2) a strong tendency, compared to inferences, to select information about both alternatives with actions.

Experiment 2 tested the second prediction. It was predicted that data suggesting that one alternative was incorrect would lead many subjects to consider, and select information about, the other alternative. For actions, it was predicted that this manipulation would have no effect. Again the data were as predicted.     

Hypothesis Formulation,Model Interpretation,and Model Equivalence: Implications of a Mereological Causal Interpretation of Structural Equation Models

One can distinguish statistical models used in causal modeling from the causal interpretations that align them with substantive hypotheses. Causal modeling typically assumes an efficient causal interpretation of the statistical model. Causal modeling can also make use of mereological causal interpretations in which the state of the parts determines the state of the whole. This interpretation shares several properties with efficient causal interpretations but also differs in terms of other important properties. The availability of alternative causal interpretations of the same statistical models has implications for hypothesis specification, research design, causal inference, data analysis, and the interpretation of research results.     

Children's selective trust decisions: rational competence and limiting performance factors

Recent research has amply documented that even preschoolers learn selectively from others, preferring, for example, reliable over unreliable and competent over incompetent models. It remains unclear, however, what the cognitive foundations of such selective learning are, in particular, whether it builds on rational inferences or on less sophisticated processes. The current study, therefore, was designed to test directly the possibility that children are in principle capable of selective learning based on rational inference, yet revert to simpler strategies such as global impression formation under certain circumstances. Preschoolers (N = 75) were shown pairs of models that either differed in their degree of competence within one domain (strong vs. weak or knowledgeable vs. ignorant) or were both highly competent, but in different domains (e.g., strong vs. knowledgeable model). In the test trials, children chose between the models for strength‐ or knowledge‐related tasks. The results suggest that, in fact, children are capable of rational inference‐based selective trust: when both models were highly competent, children preferred the model with the competence most predictive and relevant for a given task. However, when choosing between two models that differed in competence on one dimension, children reverted to halo‐style wide generalizations and preferred the competent models for both relevant and irrelevant tasks. These findings suggest that the rational strategies for selective learning, that children master in principle, can get masked by various performance factors.     

Testing Models of Counselor Development With a Measure of Counseling Self-Efficacy

Models of counselor development have become very popular, but empirical research has found differences primarily between beginning graduate students and doctoral interns. In the research described here, a counseling self-efficacy instrument was developed and was used to test hypotheses based on self-efficacy theory and models of counselor development, both of which would make similar predictions about increases in counseling self-efficacy resulting from clinical training and experience. The findings include strong reliability and validity evidence for the instrument and several significantly different groups of participants that correspond roughly to the groups hypothesized in stage models of counselor development.